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Re: st: interpretation for negative and positive slope combination of interaction term


From   Nahla Betelmal <nahlaib@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: interpretation for negative and positive slope combination of interaction term
Date   Thu, 9 May 2013 21:06:49 +0100

Sorry tyaqub2003, I cant see the content of your email. Can you resend
it again please.

thank you

nahla

On 9 May 2013 20:51,  <tyaqub2003@yahoo.com> wrote:
> Sent from my BlackBerry® smartphone provided by Airtel Nigeria.
>
> -----Original Message-----
> From: Nahla Betelmal <nahlaib@gmail.com>
> Sender: owner-statalist@hsphsun2.harvard.edu
> Date: Thu, 9 May 2013 20:51:21
> To: <statalist@hsphsun2.harvard.edu>
> Reply-To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: interpretation for negative and positive slope combination of
>  interaction term
>
> Thanks for the reply David, but I think there is something not quite
> right. If you check this file, p.133 figure 7.8
>
> http://www.sagepub.com/upm-data/21120_Chapter_7.pdf
>
> You will notice that in order to get the slope for the group with
> Dummy= 1 (overconfident manager in my case), we should add the
> coefficient of beta and gama ( MV and OC*MV in my case) , and to get
> the intercept for those managers we should add the alpha and y ( my
> model intercept and the coefficient of OC )
>
>  I found the same in other files, the problem is that all the examples
> they provide both beta and gama are positive which makes the addition
> and interpretation process easy.
>
>  my question is how to add and interpret when one is positive and the
> other is negative . As the effect of MV on realistic managers is -
> 0.0566  and on  overconfident managers is + 0.0596 , it moves from
> negative to positive 0.003 (again the interaction is significant
> although at 10 level). how many times MV effect overconfident managers
> more than other managers ?
>
> I would really appreciate help in that
>
> Many thanks
>
> Nahla
>
> On 9 May 2013 19:36, David Crow <david.crow@cide.edu> wrote:
>> Dear Nahla-
>>
>> You're on the right track, but not quite right.  I find that it's
>> helpful to think of the meaning of each coefficient.  Let's boil your
>> model down to just the two variables (Market Value, MV, and
>> Overconfident Managers, OC), their interaction, and an intercept:
>>
>> y = B0 + B1*(MV) + B2*(OC) + B3*(MV*OC) + u
>> and yhat = B0 + B1*(MV) + B2*(OC) + B3*(MV*OC)
>>
>> Since OC is an indicator variable (overconfident = 1), when OC=0--that
>> is, for non-overconfident, or "realistic" managers", yhat is simply B0
>> + B1*(MV) and the effect of market value is given by B1.  However,
>> when OC=1--that is, for overconfident managers--yhat is
>> B0+B1*MV+B2*OC+B3*MV*OC. Since OC=1, this simplifies to
>> B0+B1*MV+B2+B3*MV and the effect of MV is given by B1+B2+B3
>>
>> Your calculation (-0.0566241 + 0.0596146= 0.003) leaves out the term
>> B2, the coefficient for OC.  So, the correct slopes are:
>>
>> OC=0:  -0.0566241
>> OC=1:  -0.0566241 + -.1040174 + 0.0596146 = -.1010269.
>>
>> In this case, the effects of market value appear to attenuate the
>> effects of overconfidence.
>>
>> Hope this helps.
>>
>> Best,
>> David
>>
>> On Thu, May 9, 2013 at 8:20 AM, Nahla Betelmal <nahlaib@gmail.com> wrote:
>>>
>>> Dear Statalist,
>>>
>>>
>>> As you can see below, I have a interaction term between OC (dummy =1
>>> for overconfidence) and MV (continuous variable for market value). The
>>> interaction term is positive and significant. I want to calculate the
>>> slope against MB for overconfident managers which should be the
>>> coefficient of MV plus the coefficient of OC*MV.
>>> I am confused how to get this figure because MV is negative and OC*MV
>>> is positive. So, Should it be -0.0566241 + 0.0596146= 0.003? if this
>>> is true how can I interpret how many times the effect of MV is larger
>>> for overconfident managers??  0.003/0.0566.
>>>
>>> I am really confused and I highly appreciate your help please
>>>
>>>
>>>
>>>
>>> Linear regression                                      Number of obs =      49
>>>                                                        F( 10,    38) =    3.23
>>>                                                        Prob > F      =  0.0043
>>>                                                        R-squared     =  0.4385
>>>                                                        Root MSE      =  .08529
>>>
>>> ------------------------------------------------------------------------------
>>>              |               Robust
>>> earnings managment|      Coef.   Std. Err.      t    P>|t|     [95%
>>> Conf. Interval]
>>> -------------+----------------------------------------------------------------
>>> var1 |   .0081153   .0058432     1.39   0.173    -.0037137    .0199443
>>> MV |  -.0566241   .0353602    -1.60   0.118     -.128207    .0149588
>>>   var3|   .1992782    .093338     2.14   0.039     .0103252    .3882312
>>>  var4 |  -.0040891   .0109331    -0.37   0.710    -.0262219    .0180437
>>>    var5 |   .0817256   .1169071     0.70   0.489    -.1549405    .3183917
>>>   var6 |   .0291373    .026944     1.08   0.286    -.0254079    .0836825
>>> var7 |  -.0646094   .0320074    -2.02   0.051     -.129405    .0001863
>>> var8 |  -.0867868   .0311875    -2.78   0.008    -.1499227   -.0236509
>>>         OC|  -.1040174   .0556577    -1.87   0.069    -.2166906    .0086558
>>>  OC*MV |   .0596146   .0324333     1.84   0.074    -.0060433    .1252724
>>>        _cons |   .1643745   .0994735     1.65   0.107    -.0369991     .365748
>>>
>>>
>>> many Thanks
>>>
>>> Nahla Betelmal
>>> *
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>>
>>
>>
>>
>> --
>> Web site for México, las Américas y el Mundo:
>> http://mexicoyelmundo.cide.edu/
>>
>> ====================================
>> David Crow
>> Profesor-Investigador/Assistant Professor
>> División de Estudios Internacionales
>> Carretera México-Toluca 3655
>> Col. Lomas de Santa Fe 01210  México, D.F.
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>>
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>>
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